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The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality goals have proposed by many countries, including China. Meanwhile, China has also proposed the construction of a new-type electric power system with new energy as the main body, which will have great impacts on coal-fired power generation enterprises. The transformation of coal-fired power enterprises is imperative, and the evaluation of the organizational performance of coal-fired power enterprises is urgent, which can accurately determine the development status and even can find issues related to the transformation of coal-fired power enterprises. In this paper, a hybrid evaluation model is proposed based on the Variational Auto-Encoder algorithm and fuzzy comprehensive evaluation method improved by a vague set to comprehensively evaluate the organizational performance of coal-fired power enterprises. Eight coal-fired power enterprises in North China are selected for empirical analysis. The results show that the YC coal-fired power enterprise has the best organizational performance, while the NK coal-fired power enterprise has the worst organizational performance. Moreover, sensitivity and comparative analyses are carried out to verify the robustness of the evaluation result using the proposed hybrid method in this paper. This paper focuses on the organizational performance evaluation of coal-fired power enterprises, which can provide a reference for the smooth transformation and sustainable development of coal-fired power enterprises in the context of "Dual-Carbon" goals. [ABSTRACT FROM AUTHOR]

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. Therefore, ensuring frequency stability and system security will emerge as pivotal challenges in the future development process. Created by combining a Li-ion battery and a supercapacitor, a hybrid energy storage system (HESS), which possesses robust power regulation capabilities and rapid response capabilities, holds promise for supporting the frequency stability of power systems. In this context, the assessment of the economic viability of HESSs providing multitype frequency response services becomes a critical factor in their deployment and promotion. In this paper, an economic analysis approach for a Li-ion battery–supercapacitor HESS towards a multitype frequency response is presented. First, a multitype frequency response-oriented operational mode for the HESS is designed, outlining the roles and functions of the Li-ion battery and the supercapacitor in delivering distinct services. Moreover, building upon the analysis of the power trajectory of Li-ion batteries, a lifetime model for the HESS is proposed based on the rain-flow counting method. Furthermore, considering the competitive landscape for the HESS in the frequency regulation ancillary service market, a full lifecycle economic assessment model is proposed. Finally, case studies on actual power system frequency data and PJM market data are performed to verify the effectiveness of the proposed method, and the simulation results confirm that the HESS exhibits robust performance and a competitive advantage in providing multitype frequency response services. Additionally, it demonstrates commendable economic benefits, establishing its potential as a valuable contributor to frequency response services. [ABSTRACT FROM AUTHOR]

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. How to ensure the accommodation of renewable energy will also be the core issue in the future development process of renewable-dominated electric power systems. In this context, shared energy storage (SES), a novel business model combined with energy storage technologies and the sharing economy, has the potential to play an important role in renewable energy accommodation scenarios. This paper systematically organizes the application prospect, development status and key technologies of SES in the renewable energy accommodation scenario in the context of China, providing helpful references for the promotion of the business model. Firstly, a typical SES framework for renewable energy accommodation is described, and three basic forms of SES in this scenario are presented. Moreover, the application prospect of SES in the renewable energy accommodation scenario is quantitatively analyzed based on the renewable energy generation planning under the carbon peaking goal and the current guarantee mechanism of renewable energy accommodation. Furthermore, the rules for energy storage systems that provide the peak-regulation ancillary service in typical regions and provincial administrative regions in China are summarized, and the development status of SES in the renewable energy accommodation scenario is analyzed, combined with the actual market data. Finally, the key technologies to promote the further development of SES for renewable energy accommodation are presented. [ABSTRACT FROM AUTHOR]

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of high penetration DG in the distribution network operations, it is necessary to evaluate the acceptance capacity of DG. The correct evaluation can realize the secure, economic and low-carbon configuration of DG. In this paper, an evaluation method of acceptance capacity of DG in the distribution network considering the carbon emission is proposed. Firstly, a multi-objective evaluation model of acceptance capacity of DG is constructed with the objectives of minimizing carbon emission in the full life cycle, minimizing node voltage deviation and maximizing line capacity margin. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the model to determine the Pareto optimal solutions of DG configuration. Then, the comprehensive index of acceptance capacity evaluation is obtained based on entropy weight method to decide the optimal compromise solution. Finally, an actual 55-bus distribution network in China is used to verify the effectiveness of the proposed method. The simulation results show that the proposed evaluation method can comprehensively obtain the optimal compromise solution considering the reliability, economy and carbon emission benefits of distribution network operation, which guides the DG configuration in the distribution network. [ABSTRACT FROM AUTHOR]

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems. [ABSTRACT FROM AUTHOR]

Optimal utilization of available water resources has become more urgent due to the rapid growth of the economy and population. The joint operation of the Three Gorges cascade and Qingjiang cascade reservoirs in China was studied in this paper. Choosing maximization of hydropower generation and hydropower revenue as objective functions respectively, optimal models were established for individual and joint operation of the cascade reservoirs. The models were solved by the progressive optimality algorithm. The storage and electric compensation benefits among cascade reservoirs were analyzed. The daily inflow data of consecutive hydrological years of 1982-1987 were selected for a case study. Compared with the design operation rule, the joint operation of the multi-reservoir system can generate 5.992 billion kWh of extra power or an increase of 5.70% by the objective function of maximum hydropower generation. Through reservoir storage compensation, the spilled water of the Three Gorges and Qingjiang cascade reservoirs was decreased by 78.741 and 5.384 billion m3, respectively. [ABSTRACT FROM AUTHOR]

An important goal of China's electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP) and each rival's bidding strategy as common knowledge (in dynamic game market models), and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models), which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC) algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n). The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant's profit or social welfare compared with traditional reinforcement learning methods. [ABSTRACT FROM AUTHOR]

Environmental regulations are the key measure by which governments achieve sustainable environmental and economic development. This study aimed to determine the direct and indirect impacts of environmental regulations on total factor energy efficiency of regions in China. Since regions have different levels of economic development and resource endowment, we used the slacks-based measure (SBM)-undesirable model to calculate total factor energy efficiency considering regional technology heterogeneity and examined the regional impacts of environmental regulation on this efficiency using the Tobit regression model. A positive direct impact was generated in the eastern region of China by the forced mechanism, which forced enterprises to reduce fossil fuel energy demand and increase clean energy consumption; whereas a negative direct impact was generated in the middle and western regions owing to the green paradox, which is the observation that expected stringent environmental regulation prompts energy owners to accelerate resource extraction. Moreover, indirect impacts through technological progress and foreign direct investment were taken into account in the model, and the results show that the indirect impacts vary across regions. A logical response to these findings would be to develop different policies for different regions. [ABSTRACT FROM AUTHOR]

The low-carbon transition of the power system is essential for China to achieve peak carbon and carbon neutrality. However, China could suffer power shortages due to radical policies in some extreme cases. The gap between power demand and supply from March 2021 to November 2021 ranged between 5.2 billion kW·h and 24.6 billion kW·h. The main reason for the power shortage was over-reliance on renewable energy and insufficient coal power supply for the power system. The low-carbon transformation path of the electric system needs to be explored with more flexibility for power security. This study applied a modified LEAP model and carried out a forecast analysis of thermal power generation and installed capacity in 2025 and 2030 under normal and extreme weather scenarios. The results suggested that: the installed capacity of thermal power will need to account for about 44.6–46.1% of power generation in 2025 and 37.4–39.3% in 2030, with the assumption of power shortages caused by the instability and uncertainty of renewable power. In the future, China needs to pursue the development of diversified energy sources and enhance the power supply security capability while strengthening the development and utilization of renewable energy. [ABSTRACT FROM AUTHOR]

In view of the great contribution of coal-fired units to CO2 emissions, the coupled coal and power system with consideration of CO2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO2 mitigation strategy associated with stochastic power-generation demand under specific CO2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO2 emission mitigation, as well as facility capacity upgrade and expansion under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO2 emission reduction could be analyzed. [ABSTRACT FROM AUTHOR]

Depletion of fossil fuel deposits is the main current issue related to the world's power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development. [ABSTRACT FROM AUTHOR]